Tflite full Integer quantization bug.

Hi, I am trying to quantize my own tf-keras model for NNAPI delegate. First the origin tf-keras model size is 5.6MB,and quantized int8-tflite model size is about 1.8M, that is not 4X times. And more important is : i am trying to run the quantized int8-tflite model on this test benchmark (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/benchmark), the result is : use_nnapi=true inference time is longer than use_nnapi=false(202883us / 45882.2us). And i test the quantized model in tensorflow hub, which inference time are fine on my mobile device,(3x about time reduce.), but I quantize the original SaveModel format model in tensorflow hub according to Tutorials (https://www.tensorflow.org/lite/performance/post_training_quantization). the result is same as my own quantized int8-tflite model. such as moblenet_v2_130_224: (size:21.6/6.3)(time(nnapi true/false):104323 / 42137.5 ). The other model is the same as mine. Meanwhile , I quantized mnist example, finally get the same result(nnapi=true , inference time is longer than nnapi=false.). and I want to quantize the given example of quantized model and original model, but which are not SaveModel format , so I can not load the model to process. So , are there problems in my quantization process?

System information

• Have I written custom code (as opposed to using a stock example script provided in TensorFlow):
• OS Platform and Distribution :macos BigSur 11.5.1
• Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: DM-AI-V1.1(haveing one NPU)
• TensorFlow installed from (source or binary):
• TensorFlow version (use command below): 2.5
• Python version: 3.7
• Bazel version (if compiling from source):
• GCC/Compiler version (if compiling from source):
• CUDA/cuDNN version:
• GPU model and memory:

Thx.